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Rate Curve Analytics

rate_curve
Read-only

Return official government-nominal curve nodes, slopes, or derived forward segments for one currency. This is a subscriber data tool and preserves the endpoint's requested view and method metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional target date, YYYY-MM-DD.
viewNoCurve view: nodes, slopes, or forwards.nodes
methodNoForward derivation method when view is forwards.derived_from_spot_nodes
metricNoCurve metric.spot
currencyYes3-letter currency code.
curve_familyNoOfficial curve family.government_nominal

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds meaningful behavioral disclosure beyond that: it is a subscriber-only tool and it 'preserves the endpoint's requested view and method metadata.' This gives the agent useful operational context about permissions and response fidelity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, both information-dense. The first sentence states the action and outputs; the second adds access and metadata context. Every word earns its place, with no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (6 parameters, 1 required) and the presence of an output schema and annotations, the description is mostly complete. It conveys the core functionality, access requirements, and output categories. It lacks a note about date handling or data availability, but these are covered by the schema and output schema, so the description is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explicitly naming the view options ('nodes, slopes, or derived forward segments') and limiting to 'one currency,' which reinforces the 'view' and 'currency' parameters. It also mentions 'method metadata' which highlights the 'method' parameter's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'return' and the specific resource: 'official government-nominal curve nodes, slopes, or derived forward segments for one currency.' This distinguishes it from sibling tools like rate_differentials by specifying the exact data type and scope. It is unambiguous and action-oriented.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context that this is a subscriber data tool, implying access restrictions, and specifies it returns data for one currency. However, it does not explicitly mention when to use this versus alternatives or provide exclusions, so it misses the highest bar for usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions; the visual_artifact variants are explicitly duplicate payloads for chart rendering. However, several task and analysis tools (macro_briefing_task, macro_research_pack_task, indicator_intel_task) have overlapping scopes and could cause misselection despite different outputs.

Naming Consistency4/5

Tool names are consistently snake_case with systematic _task and _visual_artifact suffixes, making the pattern predictable. Minor deviations like 'ping', 'subscribe_for_mcp_access', and a few noun-only names (e.g., 'forex', 'commodities') break a strict verb_noun pattern but remain readable.

Tool Count2/5

At 48 tools, the surface is far beyond the typical well-scoped server and risks overwhelming agents. The broad macro/FX domain justifies some size, but 48 is excessive and could be consolidated (e.g., merging visual artifact pairs or grouping task tools).

Completeness5/5

The tool set covers the full macro/FX workflow: data discovery (data_catalogue), raw queries (indicator_query, forex, commodities), visual artifacts, release calendar, news, COT, sentiment, seasonality, backtesting, scenario modeling, portfolio risk, and reference tools. No obvious dead ends or missing lifecycle operations for a read-heavy data server.